![Status](https://img.shields.io/badge/Status-Incomplete-orange.svg)
Artificial Intelligence vs Machine Learning vs Deep Learning
- Artificial Intelligence(AI) is any technique which enables machines/computers to mimic human behaviour
- Machine Learning(ML) is subset of AI that use Statistical methods for machines/computers to learn without being explicitly programmed and improve with experience
- Deep Learning is subset of ML that use Multi-Layer Neural Network computation more feasible
Knowledge needed to master in this area
Mathematics (http://www.deeplearningbook.org/)
Type of Machine Learning Algorithms
- Supervised Learning (From a target (dependent variable) to be predicted from given set of predictors (independent variables)
- Unsupervised Learning (No target (dependent variable) usually used for clustering)
- Reinforcement Learning (Machine is trained to make decisions based on algorithm to learn from previous experience)
![Logo Title Text 1 alt text](https://s-media-cache-ak0.pinimg.com/736x/8b/23/3e/8b233e2d7f26b00d0c594894917a127b--supervised-learning-variables.jpg)
TERMINOLOGY
- Supervised Machine Learning- Combining input from learning to produce useful predictions on unseen data
- Example = Regression problem (predict the value of house- continuous value) and Classification problem (determine which is more likely to happen (exp- Cancer)
![](/src/supervised.PNG)
- Unsupervised Machine Learning- Making sense of pattern or insight where we don’t know in advance
- Example = Social network analysis
- Label- For example in spam filtering labels could be spam or not spam (target that we want to predict)
- Features- Way we present the data
- Label example- Has {features, label}:(x,y) used in training
- Unlabeled example- has {features, no label}:(x,y) used in testing
- Model- learned by mapping example to predicted label
Arthur Samuel: Field of study that gives computers the ability to learn without being explicitly programmed.
Tom Mitchell: A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.
List of Machine Learning Algorithms (Examples)
Linear Regression
Finding straight line or hyperplane that best fits to set of points
Multi Feature Variables in Linear Regression
![](/src/Multi_feature.PNG)
Scaling and Mean Normalization
![](/src/Mean_normalization.PNG)
![Alt Text](https://raw.githubusercontent.com/mattnedrich/GradientDescentExample/master/gradient_descent_example.gif)
Comparison Gradient Descent and Normal Equation
![](/src/Compare_gradient.PNG)
Reducing loss
As we train a model reducing loss will give best trained model. Stochastic Gradient Descent involves one example at a time while for Mini-Batch Gradient Descent batches 10-1000
Type of Training models
- Static model- traine offline and exactly once
- Dynamic model- trained online and through continuous updates
What is Regression Analysis?
Regression Analysis investigates the relationship between predictor (independent variable) and target (dependent variable) where it is predictive modelling technique. Examples including time series, forecasting as well as finding casual effects relationship between variables.
Linear Regression
To estimate real values based on continous variables (using regression line represents by linear equation y=ax+b; where y=dependent variable, a=slope, x=independent variable, b=intercept)
Type of Linear Regression
- Simple Linear Regression (one independent variable)
- Multiple Linear Regression (more than one independent variables)
Python code for Linear Regression
#Import Library
#Import other necessary libraries like pandas, numpy...
from sklearn import linear_model
#Load Train and Test datasets
#Identify feature and response variable(s) and values must be numeric and numpy arrays
x_train=input_variables_values_training_datasets
y_train=target_variables_values_training_datasets
x_test=input_variables_values_test_datasets
# Create linear regression object
linear = linear_model.LinearRegression()
# Train the model using the training sets and check score
linear.fit(x_train, y_train)
linear.score(x_train, y_train)
#Equation coefficient and Intercept
print('Coefficient: \n', linear.coef_)
print('Intercept: \n', linear.intercept_)
#Predict Output
predicted= linear.predict(x_test)
R code for Linear Regression
#Load Train and Test datasets
#Identify feature and response variable(s) and values must be numeric and numpy arrays
x_train <- input_variables_values_training_datasets
y_train <- target_variables_values_training_datasets
x_test <- input_variables_values_test_datasets
x <- cbind(x_train,y_train)
# Train the model using the training sets and check score
linear <- lm(y_train ~ ., data = x)
summary(linear)
#Predict Output
predicted= predict(linear,x_test)
Other type of Regressions
- Logistic Regression
- Polynomial Regression
- Stepwise Regression
- Ridge Regression
- Lasso Regression
- ElasticNet Regression
Logistic Regression
Decision Tree
SVM
Naive Bayes
kNN
K-Means
Random Forest
Dimension Reduction Algorithm
Gradient Boosting Algorithms:
- GBM
- XGBoost
- LightGBM
- CatBoost
K-Means clustering
K-Mean clustering use a method of vector quantization for (cluster analysis). Cluster analysis is where grouping task is among similarity to each other for same group. It is an unsupervised learning.
![Logo Title Text 1 alt text](https://i.stack.imgur.com/cIDB3.png)
![](/src/kmeans-steps.png)
Steps in K-Means
- Choose K centroids (K rows randomly select)
- Assigns every data point to its closest centroid
- Recalculate the average/mean of all data points in a cluster of centroid
- Assign data points to theirs closest centroids
![](https://www.r-statistics.com/wp-content/uploads/2012/01/equation_1_RinAction2CH16.png)
Determine the best value for K
Using Elbow method, whithin a range of values of K, example (1 to 10) then calculate the sum of squared errors.
Calculation for sum of square errors
![Logo Title Text 1 alt text](http://i.imgur.com/aLKDhbF.png)
CREDIT/REFERENCES
Blog
r-bloggers-K-Means
Online Course
Machine Learning Crash Course
Website
Common-machine-learning-algorithms
Github
Siraj Raval
Big Data
Three major sources of big data
- People (social media)
- Organizations (transaction info)
- Machines (sensor)
Characteristics of Big Data
- Volume (size)
- Variety (complexity)
- Velocity (speed)
- Valence (connectedness)
- Veracity (quality)
- Value (integrating different types of data sources)
Big Data Engineering
Hadoop Ecosystem
- Enable scalibility
- Handle fault tolerence
- Optimized for a variety of data types
- Facilited shared environment
- Community supported
Big Data Ecosystem
![](/src/hadoop.PNG)
Hadoop Command Lines
- $ hadoop fs -ls / Show all directories
- $ hadoop fs -du test/mytext.txt Show size of the file
- $ cat > mytext.txt Create text file
- $ hadoop fs -mkdir test Make directory called test
-
**$hadoop fs -cat test/mytext.txt |
grep my** Search keyword #my in text file and display it |
- $hadoop fs -du s test Show size of the folder
Hadoop Components
- MapReduce - Framework for processing huge datasets of distributed problems using large number of nodes
- Flume - A service for moving large amounts of data around a cluster soon after the data is produced
HDFS is designed for:
Large files, streaming data access, and commodity hardware
What is the purpose of the shuffle operation in Hadoop MapReduce?
To transfer each mapper’s output to the appropriate reducer node based on a partitioning function
Which of the following is a duty of the DataNodes in HDFS?
Store and retrieve blocks when told to by clients or the NameNode
Which of the following is a duty of the NameNode in HDFS?
Maintain the file system tree and metadata for all files and directories
Which component determines the specific nodes that a MapReduce task will run on?
Data Lakes
- Big data storage for current and future analysis
lambda architecture
- A method to process streaming data by utilizing batch processing and real time processing
Difference between data lakes and data warehouses
- Data lakes house raw data while data warehouses contain pre-formatted data
Schema-on-read
- Data is stored as raw data until it is read by an application where the application assigns structure
Schema-on-write
- The process where data is pre-formatted prior to being read but the schema is loaded on read
streaming
- Utilizing real time data to compute and change the state of an application continuously
streaming data
- Data is unbounded in size but requires only finite time and space to process it
- Data manipulation is near real time
- Does not ping the source interactively for a response upon receiving the data
- independent computations that do not rely on previous or future data
- Small time windows for working with data
Data models
- Conceptual Data Model
- Logicsk Data Model
- Physicak Data Model
![Status](https://img.shields.io/badge/Status-Incomplete-orange.svg)
Android App Security
Android provides a sandboxed app execute env. A customized embedded Linux system interacts
with the phone hardware and an off-processor cellular radio.
Java compiler creates JVM bytecode, the Dalvik dx compiler consumes the .class files, recompiles them to
Dalvik bytecode and writes the app into single.dex file.
- The process consists of translation, reconstruction and interpretation of 3 basic elements of the app
- the constant pools, class definitions and data segment.
Constant pool: constant (references to other classes,method names, numerical constant)
Class definitions: basic infos(access flags, class names)
Data segment: method code executed by target VM number of DVM registers used, local variable table,
and operand stack sizes), class and instance variable
Risks
- Rapidly developed and deployed applications
- Coarse permission systems
- Privacy invading behaviors
- malware
- limited security models
Aims
- Design Dalvik decompiler ded
- Analyze 21 million LOC
Hypothesis
- Misuse of privacy sensitive infos (phone identifiers (IMEI,IMSI,ICC-ID), geo location
- esque (in the style of) ex: Phone identifiers, e.g., IMEI, IMSI, and ICC-ID, were used for everything from “cookie-esque” tracking to accountsnumbers.
- renaissance
- posit (put in position) - Wherepossible, we identify root causes and posit the severity ofdiscoveredvulnerabilities.
- breadth (the distance between two) - we consider a breadth of concerns including both dangerous functionality and vulnerabilities.
- substantially -significant extent
Computer science terms
- IPC (Interprocess Comm)
- Intent
Differences of JVM and DVM
- Application structure
-Java apps more than one .class files
-Dalvik apps single .dex contain all classes
- Register architecture
-JVM (stack-based)
-DVM (register-based)
- Instruction set
-Java has 200 opcodes
-Dalvik has 218 opcodes
- Constant pool structure
-Java app replicete elements within .class files (referer and referent method names)
-dx compiler eliminates the replication
- Control flow structure (loops, switch statements, exception handlers)
-Java bytecode structure loosely mirrowa the source code
-Dalvik bytecode vice versa
- Ambiguous primitive types (int, float, long, dounle)
-Java bytecode variable assignments distinguish
-Dalvik use same opcodes (untyped)
- Null references
-Dalvik not specify instead use zero value constant
- Comparison of object references
-Java bytecode uses typed opcodes (ifnull and ifnotnull)
-Dalvik bytecode use more simplistic
- Storage of primitive types in arrays
-Java bytecode is unambiguous
-Dalvik opcode uses ambiguous opcodes (aget foe int/float)
Ded decompiler
- Application retargetting (recovering typing info,translating constant pool and retargeting the bytecode)
-Type inference
- identify class and method constants and variables
- infers register types by observing how they are used in subsequent operation with known type operands
-Constant pool conversion
-Java maintains for each class, Java bytecode uses constant pool for most references
-Dalvik uses single constant pool, Dalvik bytecode places primitive type constant in bytecode
-Method retargeting
- First process the bytecode to reorganize structures that cannot be directly retargeted
- Linearly traverse the DVM bytecode and translate to JVM
- Optimization and Decompilation
Development of Android Apk
Prerequisites
Wireless Commands
Android hacking
Security Updates
Nmap and Wireshark for Network Scanning
- nmap –iflist (display IP/MASK devices useful for route path)
- nmap ‘ip address’ (Display open port/service) / nmap ‘ip address’-‘range’/ nmap ‘ip address’ ‘/’subset/
- – exclude/ -F (fast scanning)
-
nmap -sn/ -sP/ -sA/ -PN/ -PS/ -PA/ -sP -PU/( scan using UDP ping)/-o /-v/ nmap -v -O –osscan-guess ipaddress/ -sV/ -PO/ -sU/ ‘ip address’ –packet-trace / ip.addr == ‘ip address’ |
|
arp |
- nmap(– reason,disable-arp-ping)/ (–open, display open port only)/ (-p 80, specific port)
- namp –source-port/ nmap –data-length
- -p (p1, p2) (Ipaddress), -p (ip1-ip2) (Ipaddress)
- tcp.port eq 80(display TCP traffic port traffic moving across port 80)
- ip.src == 192.168.0.1
Saving result
- nmap ipaddress >output.txt
- nmap -oN output.txt ipaddress/ nmap -oG scan.Nmap.txt ipaddress/ nmap -oX scan.nmap.xml ipaddress
Windows CMD /Netstat/ ping /tracert/ ARP
- ipconfig, cls, ipconfig /all,ipconfig /displaydns
-
netstat -a, netstat -no (Display the top active connections), netstat -ano (combine -a and -no), netstat -r(display routing table), options -t, -u, -w, and -x show active TCP, UDP, RAW, or Unix socket connections, netstat -s -p tcp -f (To have netstat display statistics (-s) about TCP traffic (-p), and also force the addresses to be displayed in FQDN format (-f)), netstat -e -t 5(displaying the network interface statistics (-e) and have them updated every 5 seconds we will use (-t 5), netstat -an 1 |
find “80”(check every second and print the results if a process starts listening on TCP port 80), ping -t ipaddress(continuously send packets until the command to terminate is entered),ping -n 30 -l 1000 ipaddress(number of echo counts to 30, each having a size of 1000 bytes) |
- tracert -h ipaddress, route PRINT, netstat -r
- arp -a, arp -av, arp -a -N ipaddress
Creating Windows Payload with Msfvenom
AlienVault/ DVWA/ OSForensics/ ProDiscover Basic
References
- https://securityboulevard.com/2019/07/cyber-security-roundup-for-june-2019/
- https://www.hackingarticles.in
Terminologies
- Asset (Any data, component, device that supports information-related that should be protected)
- Vulnerability (Weakness or flaw on the asset)
-Threat (possible danger tothe system)
- Exploit (Takes advantages of vulnerability in an asset)
- Risk (Risk= Threat * vulnerabiities * impact)
Penetration Test
Set of methods and procedures for testing or protecing security of an organization.
Vulnerability Assessments
To check for vulnerabilities and document accordingly.
Rules of Engagement
- Milestones (keep track progress)
- Methodologies (OSSTMM, NIST, OWASP)
Security Analyst Tracks
Auditing and Incident Response
- Legal and regulatory compliance
- Vulnerability Scanning
- PenTest Testing (Black box, White box, Grey box
Security Threat
Categories
- STRIDE model, including spoofing, tampering, repudiation, information disclosure, denial of service, and escalation of privilege
Mitigate escalation of privilege threats
- Auditing, log monitoring and certificate authority signing
Mitigate brute force attacks
- Attempt limiting, HMAC, salted hashes and rate limiting
Slowloris attack (Dos attack //An attack that renders systems unresponsive)
Causes lead to security misconfiguration threats
- Open directories, weak defaults, open configuration pages and sample code
# Spoofing
- MITM (identity spoofing used to intercept communication)
- Email spoofing (DMARC is the Domain-Based Message Authentication, Reporting, and Conformance which helps detect and prevent email spoofing, SPF is the Sender Policy Framework, helps detect forged sender addresses, DKIM is the Domain Key Identified Mail)
- Location spoofing
# Integrity and Tampering Threats
- salami attack, where the data is broken into smaller modifications to avoid detection
# Authentication and Non-repudiation
- non-repudiation //An undeniable transaction
# Information Privacy and Confidentiality
- Properties of data //Data protection, confidentiality, copyright
# Nmap
- Check local interfaces and routes // nmap –iflist
- Scanning // nmap -sn IPADDRESS
- Sudo scanning // sudo -sA IPADDRESS
- Saving scan to file // nmap #anything > $(date + %F) #anything.txt
# Network IDS
- Snort, Bro/Zeek, Suricata
# Host IDS
# IDS with Snort
- sudo apt install snort
- sudo snort -v
- sudo nano /etc/snort/rules/local.rules //alert icmp any any -> #localnetwork any (msg:”ICMP”; sid:1;)
- sudo snort -T -c /etc/snort/rules/local.rules
- sudo snort -d -l ./ -c /etc/snort/rules/local.rules
- tail alert
# IDS with Bro
- sudo apt install bro bro-aux bro-common bro-pkg broctl
- sudo nano /etc/bro/node.cfg //change interface
- sudo nano /etc/bro/network.cfg //change local network
- sudo broctl
- install
- deploy
- ls /var/log/bro/current/
# Evading IDS with Nmap //-n skipping DNS resolution
- sudo nmap -n -D#decoyaddress,#decoyaddress #targetaddress (Decoy Scan)
- sudo nmap –scan-delay 973ms #targetaddress (Slow Scan)
- sudo nmap -sI #intermediateaddress -n #targetaddress
# Brute force analysis
- sudo nmap -p 143,993 –script imap-brute #targetaddress
- sudo nmap –script dos -Pn #targetaddress //-Pn skip host discovery
- sudo nmap –max-parallelism 512 -Pn –script-slowloris –script-args http-slowloris.runforever=true #targetaddress
# Nmap script with its test
- auth-spoof //Test for Identd malware
- smtp-strangeport //Test for mail service malware
- http-malware-host //Test for web server malware
# User account discovery
- id, w, who, groups, users
- lslogin -u, last
- cat /etc/shadows, cat /etc/passwd
- awk -F: ‘($2==””) {print $1 }’< /etc/passwd
# Firewall
- Network host- based
- Dual- homed host
- Router based firewall
- Screen host
# VPN
- Point-to-point Tunneling Protocol (EAP, CHAP)
- Layer 2 Tunneling Protocol (EAP, CHAP, MS-CHAP, PAP, SPAP, Kerberos)
- IPSec (Tunnel mode, Transport mode)
- SSL/TLS
# Penetration Testing
- Port scanning (Nmap, Sparta, Masscan, Dmitry)
- Firewall scanning (Nessus, Metasploit)
- Vulnerable scanning
# Type of PenTest
- Planning and information gathering
- Active information gathering
- Gain access using discovered exploits
- Maintain access and collect restricted data
- Cover footprints and delete logs
# Buffer Overflow Exploits
## Prevention
- Stack canaries
- Non- executable stack policy
- Randomize memory stack
Privilege Escalation Attacks
- Horizontal Privilege Escalation
- Vertical Privilege Escalation
Client- side Attacks
- Clickjacking
- Cross Origin Resource Sharing
- Cross-site Scripting (XSS)
- Form Hijacking
- Open redirection
- HTML injection
- Malware
Incident Response Stages
- Preparation
- Identification
- Containment
- Eradication
- Recovery
- Lessons Learned
- Sleuth Kit (digital forensic)
- Metasploit Tollkit
- Websense (reduce client side attack)
- FireEye Security Orchestrator
- Sancp (Network connection profiler)
- Nagios (packet logging)
- Squid/DansGuardian
- Base (alert console for Snort)
- Nmap
- Squill (Network security monitoring)
- Wireshark
OODA Loop (Observe, Orient, Decode, Act)
Malware
- Virus (Install w/o user consent)
- Worm (Self-propagating over network)
- Trojan
- Spyware/adware
- Ransomware
Antimalware
Security awareness
Digital forensics hardwares
- Write blocker
- Forensic RAID disk array
- Mobile device extractor
- Forensic duplicator
Digital forensics softwares
- dd / disk dump command
- Forensic toolkit /FTK
- EnCase
- BlackBag MacQuisition
- x1 Social Discovery
- Helix, Cellerite, Sysinternal Suite
- md5sum
- get-filehash
- Logcat
- ClockworkMod Recovery
- Linux Memory Extractor
- ANdroid SDK
- iTunes Backup
- iPhone Analyzer
- iPhone Explorer
-
Lantern Forensics
- nc -l -vvv -p 8888 > filename.dd
- volatility -f filename.dd imageinfo, pslist, connscan, clipboard
Reference: https://www.practice-labs.com/
Nmap
- nmap –top-ports 10 192.168.0.1 (top 10 used ports)
- nmap -sU -p 53,80,3389 192.168.0.1 (selective UDP ports)
- nmap -sT 192.168.0.1 (port scanning using TCP connect)
- nmap -F 192.168.0.1 (scan for the 100 common ports)
- nmap -p 1-100 192.168.0.1 (scan for the range of port)
- nmap -sn -PS80 192.168.0.0/24 (send the SYN message to a specific port)
- nmap -p 80 192.168.0.0/24
- nmap -PR 192.168.0.0/24 (send the ARP requests)
- nmap -sP 192.168.0.0/24 (Using ping for discovering a host)
- nmap 192.168.0.* (wildcard to scan an IP range)
- nmap –traceroute 192.168.0.0/24 (trace the path)
- nmap -sP 192.168.0.0/24 (ping scan)
- nmap -sn 192.168.0.0/24 (scan without ping)
- nmap -O 192.168.0.3 (operating system detection)
- nmap -O –osscan-limit 192.168.0.0/24 (skip the hosts that are not up and running and scan for the operating system only on the live hosts)
- nmap -O –osscan-guess 192.168.0.3 (attempt to detect the operating system)
- nmap -A 192.168.0.6 (perform fingerprinting)
Hping
- hping3 192.168.0.1 –icmp (ICMP discovery of a single host)
- hping3 192.168.0.1 –icmp -c 5 (discovery for a limited number)
- hping3 intranet –scan 80 -S (scan for a specific TCP port)
- hping3 intranet –scan 22,80,443 -S (scan for multiple ports)
- hping3 192.168.0.1 –scan 1-80 -S (scan for a range of ports)
- hping3 192.168.0.1 –scan 1-65535 -S (scan the entire TCP port range)
- hping3 -8 0-100 -S 192.168.0.1
-8 = Enable SCAN mode.
0-100 = Range of ports to scan.
-S = set SYN flag
Nslookup
- nslookup *domain
- nslookup -type=A domain (check for any A records for domain)
- nslookup -type=soa domain (display the authoritative (primary) name server)
- nslookup -type=A -debug domain (verify how long a record is cached)
- nslookup -query=MX domain (details of the mail server)
- nslookup -type=ns domain (NS record maps a domain name to a list of the DNS servers that are authoritative)
- nslookup domain (name resolution using a specific DNS server)
- nslookup -timeout=10 domain (change the default timeout to wait for a reply)
Setup Honeypot
- apt-get install kali-archive-keyring
- wget http://downloads.sourceforge.net/project/pentbox18realised/pentbox-1.8.tar.gz
- tar -zxvf pentbox-1.8.tar.gz
- cd pentbox-1.8/
- ./pentbox.rb
Penetration Testing
- Planning (project scope, logistics & preliminary activities)
- Reconnaisance (passive and active info gathering)
- Scanning (deeper than reconnaisance, vuln assessment)
- Gaining access
- Maintaining access
- Covering tracks
- Analysis
- Reporting
Cyber attack
- Reconnaisance
- Scanning
- Gaining aceess
- Maintaining access
- Covering tracks
- Nmap, nikto, openVAS, SQLmap, Nessus
- Hashcat, Medusa, THC-Hydra, CeWL, John the ripper, Cain and abel, mimikatz, Patator, Dirbuster, W3AF
- MSA (Mater service agreement)
- NDA (Non-disclosure agreement)
- SOW (Statement of work)
Resources
- Swagger (REST API doc)
- WSDL/WADL (WSDL or WADL documents are XML documents that describe the SOAP-based or RESTful Web services)
Rules of engagement
- defines how penetration testing will be conducted
- timeline, targets (Locations,Systems,Applications,Third-party service provider), data handling, resources
Compliance-based Assessments
- PCI-DSS (deal with credit, debit, and cash card transactions)
- HIPAA (applies to health care providers and organizations)
- SOX (public companies, financial and IT)
- GLBA (applicable to financial organizations)
- FIPS 140-2 (applicable to the hardware, software, and firmware solutions)
Google Hacking
- site: URL report
- link URL report
- filetype: pdf report
- intitle: report
Harvester
- theHarvester -d URL -l 500 -b google
(-d parameter is used for the domain name. The -l parameter is used for the number of results. The -b parameter is used for the source of information)
Recon-ng
- recon-ng
- keys list
- modules load hackertarget
- options set SOURCE URL
Wireless
- iw dev
-
iwlist wlan0 sacn |
grep SSID |
- wpa_passphrase > .conf
- wpa_supplicant -Dwext -iwlan0 -c.conf
EFS
Bitlocker
fdisk -l
apt-get install cryptsetup
cryptsetup -y -v luksFormat /dev/sdb
cryptsetup -v status
mkfs.ext4
unmount
cryptsetup luksClose
cyrptsetup luksOpen
Hashing
SSL
- nmap -sV –script ssl-enum-cipher -p 443 (public facing IP)
Configuration Management Process
- Planning
- Identify
- Provision control
- Monitor, logging, accounting
Server side attack
- Forged PKI cert
- cryptographic downgrade
- Directory traversal
- injections attack
- HTTP intercept
- poor error handling
client side attack
- javascript
- browser extension
- MITM
- web browser escape
- VM hopping or escape
Security Principle
- CIA Triad (Confidentiality, Accessibility and Integrity)
- Risk management process (risk assessment, analysis, mitigation, remediation and communication)
Incident Response
- Preparation
- Detection and analyst
- Containment, eradication and recovery
- Post incident activity
Access control concept
Networking
- Hub
- Switch
- Router
- Firewall
- Server
- Endpoint
Ports
Type of threats
- Spoofing
- Phishing
- DDOS
- Virus
- Worm
- MITM
- Side Channel
- APT
- Insider Threat
- Malware
- Trojan
Network design
- Network segmentation
- DMZ
- VLANs
- VPN
- Defense in depth
- NAC
Data security
- Data handling (create, store, use, share, archive, destroy)
Cloud computing
- Deliver of computing services over the internet.
- Public, private, hybrid, and multi-cloud
- IaaS (Lift-and-shift migration, Testing and development)
- Paas (Development framework, Analytics or business intelligence)
- SaaS (Email and messaging, Business productivity applications, Finance and expense tracking)